• The Korean Journal of Archival Studies
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• 제28호
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• pp.3-33
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• 2011

The Office of Crown Properties was established to manage the property of royal properties as an institution belonging to the Department of the Royal Household in April, 1895. However, as the Great Han Empire established and various policies enforcing the power of the emperor became introduced, the Office of Crown Properties came to be expanded to a large financial agency that would be in charge of various financial sources such as Public Land and Maritime Tax. As the Office of Crown Properties came to manage various income sources, it classified the documents dealing with various government agencies in the Capital and other countryside regions by the unit of Section. The Office of Crown Properties classified the documents by Section and filed them according to Sending/Receiving subject. Sometimes, it filed one kind of document only but sometimes many different kinds of documents were filed together. The types of the document can show the characteristics of the document and the hierarchy of the related agencies through the document name. The fact that they filed the documents with different grades in one file shows that the hierarchy of the agency they dealt with was not the primary standard of the filing and that they did not file the documents by its type. The Office of Crown Properties did not file the related documents in the same file, either. We can say the documents are related if they were corresponded with other agencies while they dealt with a specific item. However, they did not file the related documents in the same file but distinguished sending documents from receiving documents. The reason why they filed different kind documents in the same file and separated the related documents in different file was they took 'whether they were sent or received' as the primary filing standard. They separated the sending documents from the receiving documents first and then filed them according to time regardless of the region or institution. The Office of Crown Properties primarily classified the documents by Section, classified the documents with the standard of whether they were receiving documents or sending documents and then filed them in a file according to the time. It means that the Office of Crown Properties came to create the Official Document Classification and filing system.

• The Journal of Bigdata
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• 제6권1호
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• pp.83-89
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• 2021

Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.

• Journal of Intelligence and Information Systems
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• 제29권4호
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.